{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import numpy.random as rng\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prices(symbol):\n",
    "    start, end = '2007-05-02', '2016-04-11'\n",
    "    data = web.DataReader(symbol, 'google', start, end)\n",
    "    data=pd.DataFrame(data)\n",
    "    prices=data['Close']\n",
    "    prices=prices.astype(float)\n",
    "    return prices\n",
    "\n",
    "def get_returns(prices):\n",
    "        return ((prices-prices.shift(-1))/prices)[:-1]\n",
    "    \n",
    "def get_data(list):\n",
    "    l = []\n",
    "    for symbol in list:\n",
    "        rets = get_returns(get_prices(symbol))\n",
    "        l.append(rets)\n",
    "    return np.array(l).T\n",
    "\n",
    "def sort_data(rets):\n",
    "    ins = []\n",
    "    outs = []\n",
    "    for i in range(len(rets)-100):\n",
    "        ins.append(rets[i:i+100].tolist())\n",
    "        outs.append(rets[i+100])\n",
    "    return np.array(ins), np.array(outs)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "symbol_list = ['C', 'GS']\n",
    "rets = get_data(symbol_list)\n",
    "ins, outs = sort_data(rets)\n",
    "ins = ins.transpose([0,2,1]).reshape([-1, len(symbol_list) * 100])\n",
    "div = int(.8 * ins.shape[0])\n",
    "train_ins, train_outs = ins[:div], outs[:div]\n",
    "test_ins, test_outs = ins[div:], outs[div:]\n",
    "\n",
    "#normalize inputs\n",
    "train_ins, test_ins = train_ins/np.std(ins), test_ins/np.std(ins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "positions = tf.constant([-1,0,1]) #long, neutral or short\n",
    "num_positions = 3\n",
    "num_symbols = len(symbol_list)\n",
    "num_samples = 20\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, num_symbols * 100])\n",
    "y_ = tf.placeholder(tf.float32, [None,  num_symbols])\n",
    "\n",
    "W = tf.Variable(tf.random_normal([num_symbols * 100, num_positions * num_symbols]))\n",
    "b = tf.Variable(tf.random_normal([num_positions * num_symbols]))\n",
    "\n",
    "y = tf.matmul(x, W) + b \n",
    "\n",
    "# loop through symbol, taking the columns for each symbol's bucket together\n",
    "pos = {}\n",
    "sample_n = {}\n",
    "sample_mask = {}\n",
    "symbol_returns = {}\n",
    "relevant_target_column = {}\n",
    "for i in range(num_symbols):\n",
    "    # isolate the buckets relevant to the symbol and get a softmax as well\n",
    "    symbol_probs = y[:,i*num_positions:(i+1)*num_positions]\n",
    "    symbol_probs_softmax = tf.nn.softmax(symbol_probs) # softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))\n",
    "    # sample probability to chose our policy's action\n",
    "    sample = tf.multinomial(tf.log(symbol_probs_softmax), num_samples)\n",
    "    # isolate the probability of the selected policy (for use in calculating gradient)\n",
    "    for sample_iter in range(num_samples):\n",
    "        sample_n[i*num_samples + sample_iter] = sample[:,sample_iter]\n",
    "        pos[i*num_samples + sample_iter] = tf.reshape(sample_n[i*num_samples + sample_iter], [-1]) - 1\n",
    "        symbol_returns[i*num_samples + sample_iter] = tf.multiply(\n",
    "                                                            tf.cast(pos[i*num_samples + sample_iter], float32), \n",
    "                                                             y_[:,i])\n",
    "        \n",
    "        sample_mask[i*num_samples + sample_iter] = tf.cast(tf.reshape(tf.one_hot(sample_n[i*num_samples + sample_iter], 3), [-1,3]), float32)\n",
    "        relevant_target_column[i*num_samples + sample_iter] = tf.reduce_sum(\n",
    "                                                    symbol_probs_softmax * sample_mask[i*num_samples + sample_iter],1)\n",
    "    \n",
    "\n",
    "\n",
    "daily_returns_by_symbol_ = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in symbol_returns.values()])\n",
    "daily_returns_by_symbol = tf.transpose(tf.reshape(daily_returns_by_symbol_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "daily_returns = tf.reduce_mean(daily_returns_by_symbol, 2) # [?,5]\n",
    "\n",
    "total_return = tf.reduce_prod(daily_returns+1, 0)\n",
    "z = tf.ones_like(total_return) * -1\n",
    "total_return = tf.add(total_return, z)\n",
    "\n",
    "\n",
    "ann_vol = tf.multiply(\n",
    "    tf.sqrt(tf.reduce_mean(tf.pow((daily_returns - tf.reduce_mean(daily_returns, 0)),2),0)) ,\n",
    "    np.sqrt(252)\n",
    "    )\n",
    "sharpe = tf.div(total_return, ann_vol)\n",
    "#Maybe metric slicing later\n",
    "#segment_ids = tf.ones_like(daily_returns[:,0])\n",
    "#partial_prod = tf.segment_prod(daily_returns+1, segment_ids)\n",
    "\n",
    "\n",
    "training_target_cols = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in relevant_target_column.values()])\n",
    "ones = tf.ones_like(training_target_cols)\n",
    "gradient_ = tf.nn.sigmoid_cross_entropy_with_logits(labels=training_target_cols, logits=ones)\n",
    "gradient = tf.transpose(tf.reshape(gradient_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "\n",
    "#cost = tf.multiply(gradient , daily_returns_by_symbol_reshaped)\n",
    "#cost = tf.multiply(gradient , tf.expand_dims(daily_returns, -1))\n",
    "cost = tf.multiply(gradient , tf.expand_dims(total_return, -1))\n",
    "# cost = tf.multiply(gradient , tf.expand_dims(sharpe, -1))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(cost)\n",
    "costfn = tf.reduce_mean(cost)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0100 cost= 1.48023 total return= 3.557313919 sharpe= 2.085654259\n",
      "Epoch: 0200 cost= 1.81074 total return= 4.276563644 sharpe= 2.507260799\n",
      "Epoch: 0300 cost= 2.07541 total return= 4.851496696 sharpe= 2.844292164\n",
      "Epoch: 0400 cost= 1.42713 total return= 3.589344025 sharpe= 2.102812290\n",
      "Epoch: 0500 cost= 1.34209 total return= 3.339767456 sharpe= 1.959243417\n",
      "Epoch: 0600 cost= 1.50117 total return= 3.775519609 sharpe= 2.214963436\n",
      "Epoch: 0700 cost= 1.57011 total return= 3.965950012 sharpe= 2.326948643\n",
      "Epoch: 0800 cost= 1.33224 total return= 3.598165035 sharpe= 2.109631062\n",
      "Epoch: 0900 cost= 0.706881 total return= 1.877704620 sharpe= 1.100140810\n",
      "Epoch: 1000 cost= 0.761505 total return= 2.024359941 sharpe= 1.185175061\n",
      "Epoch: 1100 cost= 0.911471 total return= 2.437183142 sharpe= 1.425802827\n",
      "Epoch: 1200 cost= 1.6589 total return= 4.489245892 sharpe= 2.627991199\n",
      "Epoch: 1300 cost= 1.89802 total return= 5.195552349 sharpe= 3.041148186\n",
      "Epoch: 1400 cost= 1.84092 total return= 5.077595711 sharpe= 2.972646236\n",
      "Epoch: 1500 cost= 2.0919 total return= 5.724076271 sharpe= 3.350445986\n",
      "Epoch: 1600 cost= 2.17117 total return= 5.940022945 sharpe= 3.477900267\n",
      "Epoch: 1700 cost= 1.94947 total return= 5.357582569 sharpe= 3.136368275\n",
      "Epoch: 1800 cost= 2.05009 total return= 5.628676891 sharpe= 3.295320988\n",
      "Epoch: 1900 cost= 2.31323 total return= 6.348797321 sharpe= 3.717358351\n",
      "Epoch: 2000 cost= 2.18853 total return= 6.030413628 sharpe= 3.530701399\n"
     ]
    }
   ],
   "source": [
    "# initialize variables to random values\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "# run optimizer on entire training data set many times\n",
    "train_size = train_ins.shape[0]\n",
    "for epoch in range(2000):\n",
    "    start = rng.randint(train_size-50)\n",
    "    batch_size = rng.randint(2,75)\n",
    "    end = min(train_size, start+batch_size)\n",
    "    \n",
    "    sess.run(optimizer, feed_dict={x: train_ins[start:end], y_: train_outs[start:end]})#.reshape(1,-1).T})\n",
    "    # every 1000 iterations record progress\n",
    "    if (epoch+1)%100== 0:\n",
    "        t,s, c = sess.run([ total_return, sharpe, costfn], feed_dict={x: train_ins, y_: train_outs})#.reshape(1,-1).T})\n",
    "        t = np.mean(t)\n",
    "        s = np.mean(s)\n",
    "        print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\",c, \"total return=\", \"{:.9f}\".format(t), \n",
    "             \"sharpe=\", \"{:.9f}\".format(s))\n",
    "        #print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in sample results\n",
    "#init = tf.initialize_all_variables()\n",
    "#sess.run(init)\n",
    "d, t = sess.run([daily_returns, total_return], feed_dict={x: train_ins, y_: train_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iCod3YdVNZ7UlOvTIdeKDSQK6EAE51Os6/nnRnZM6cGhINbQqDx2p/NMohwbv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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112175400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# equity curve\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#out of sample results\n",
    "d, t = sess.run([daily_returns, total_return], feed_dict={x: test_ins, y_: test_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3EnYvkzH3NWth5T2Dzj9tblEZ9WM1HJ0kYspyVyhORoIeaa1nFA8rORY7X0od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\nMz4nXB9JEY3l9K94irlsxY1sWv0kAJlHFpOlEFNGf1p0E59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tQEyRfXFaN2rt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117721908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#out of sample results\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
